library(raster)
source('https://raw.githubusercontent.com/oharac/src/master/R/common.R')
source(here('common_fxns.R'))Create taxa-level maps of number of species impacted per cell, per year.
Identify the species included in the impacted list (using the IUCN species IDs of impact map files, from prior scripts!). Join this with the list of mapped species, to identify which taxonomic group the species is in (based on the downloaded IUCN shapefiles). Taxa groups not represented in the impacted species list will be dropped.
Taxonomic groups dropped from this analysis - i.e., group does not have any species that fits the “impacted” criteria:
chameleons, cycads, magnolias, fw_caridean_shrimps, fw_crayfish, conifers, surgeonfishes, crocodiles_and_alligators, fw_crabs, cacti, tarpons_and_ladyfishes, sturgeons, lobsters, amphibians
The one vulnerable tarpon species (i.e. not LC, EX, or DD) is not sensitive to any of the stressors and is not mapped, so the whole taxon is dropped from this analysis.
These maps ignore the number of impacts occurring on each species, and count up the number of species impacted by the aggregated stressor group (land-based, ocean, climate, and fishing).
We will also map species impacts using the priority weights from script 3b, i.e. \[\text{priority} = \frac{\text{% range impacted}}{\ln (\text{spp range, km}^2)}\] Note that priority is based on mean impact over last three impacted years.
For a given taxon:
Just for the most recent year of each, back and forth to highlight diffs. To highlight the differences in pattern not magnitude, I’ll stretch the priority scale to have the same range as the count scale (i.e. 1 to the max number of critters).